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Dietary Glycemic Index and Load and the Risk of Type 2 Diabetes: Assessment of Causal Relations

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Dietary Glycemic Index and Load and the Risk of Type 2 Diabetes: Assessment of Causal Relations nutrients Review Dietary Glycemic Index and Load and the Risk of Type 2 Diabetes: Assessment of Causal Relations Geoffrey Livesey 1,* , Richard Taylor 1, Helen F. Livesey 1, Anette E. Buyken 2, David J. A. Jenkins 3,4,5,6, Livia S. A. Augustin 4,7, John L. Sievenpiper 3,4,5,6, Alan W. Barclay 8, Simin Liu 9, Thomas M. S. Wolever 3,4, Walter C. Willett 10, Furio Brighenti 11 , Jordi Salas-Salvadó 12,13 , Inger Björck 14, Salwa W. Rizkalla 15, Gabriele Riccardi 16, Carlo La Vecchia 17 , Antonio Ceriello 18, Antonia Trichopoulou 19, Andrea Poli 20, Arne Astrup 21 , Cyril W. C. Kendall 3,4,22, Marie-Ann Ha 23 , Sara Baer-Sinnott 24 and Jennie C. Brand-Miller 25 1 Independent Nutrition Logic Ltd, 21 Bellrope Lane, Wymondham NR180QX, UK; [email protected] (R.T.); [email protected] (H.F.L.) 2 Institute of Nutrition, Consumption and Health, Faculty of Natural Sciences, Paderborn University, 33098 Paderborn, Germany; [email protected] 3 Departments of Nutritional Science and Medicine, Faculty of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada; [email protected] (D.J.A.J.); [email protected] (J.L.S.); [email protected] (T.M.S.W.); [email protected] (C.W.C.K.) 4 Clinical Nutrition and Risk Factor Modification Centre, St. Michael’s Hospital, Toronto, ON M5C 2T2, Canada; [email protected] 5 Division of Endocrinology and Metabolism, Department of Medicine, St. Michael’s Hospital, Toronto, ON M5C 2T2, Canada 6 Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON M5C 2T2, Canada 7 Epidemiology, Istituto Nazionale Tumori IRCCS “Fondazione G. Pascale”, 80131 Napoli, Italy 8 Glycemic Index Foundation, 26 Arundel St, Glebe, Sydney NSW 2037, Australia; [email protected] 9 Department of Epidemiology and Medicine, Brown University, Providence, RI 02912, USA; [email protected] 10 Departments of Nutrition and Epidemiology, Harvard T. H. Chan School of Public Health and Harvard Medical School, Boston, MA 02115, USA; [email protected] 11 Department of Food and Drug, University of Parma, 43120 Parma, Italy; [email protected] 12 Human Nutrition Unit, Department of Biochemistry and Biotechnology, Faculty of Medicine and Health Sciences, Institut d’Investigació Sanitària Pere Virgili (IISPV), Rovira i Virgili University, 43201 Reus, Spain; [email protected] 13 Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 27400 Madrid, Spain 14 Retired from Food for Health Science Centre, Antidiabetic Food Centre, Lund University, S-221 00 Lund, Sweden; [email protected] 15 Institute of Cardiometabolism and Nutrition, ICAN, Pitié Salpêtrière Hospital, F75013 Paris, France; [email protected] 16 Department of Clinical Medicine and Surgery, Federico II University, 80147 Naples, Italy; [email protected] 17 Department of Clinical Sciences and Community Health, Università degli Studi di Milano, 201330 Milan, Italy; [email protected] 18 IRCCS MultiMedica, Diabetes Department, Sesto San Giovanni, 20099 Milan, Italy; [email protected] 19 Hellenic Health Foundation, Alexandroupoleos 23, 11527 Athens, Greece; [email protected] 20 Nutrition Foundation of Italy, Viale Tunisia 38, I-20124 Milan, Italy; [email protected] 21 Department of Nutrition, Exercise and Sports (NEXS) Faculty of Science, University of Copenhagen, 2200 Copenhagen, Denmark; [email protected] 22 College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, SK S7N 5B5, Canada 23 Spinney Nutrition, Shirwell, Barnstaple, Devon EX31 4JR, UK; [email protected] 24 Oldways, Boston, MA 02116, USA; [email protected] 25 Charles Perkins Centre and School of Life and Environmental Sciences, University of Sydney, Sydney, NSW 2006, Australia; [email protected] * Correspondence: [email protected]; Tel.: +44-1953-606689 Nutrients 2019, 11, 1436; doi:10.3390/nu11061436 www.mdpi.com/journal/nutrients Nutrients 2019, 11, 1436 2 of 34 Received: 3 May 2019; Accepted: 20 June 2019; Published: 25 June 2019 Abstract: While dietary factors are important modifiable risk factors for type 2 diabetes (T2D), the causal role of carbohydrate quality in nutrition remains controversial. Dietary glycemic index (GI) and glycemic load (GL) have been examined in relation to the risk of T2D in multiple prospective cohort studies. Previous meta-analyses indicate significant relations but consideration of causality has been minimal. Here, the results of our recent meta-analyses of prospective cohort studies of 4 to 26-y follow-up are interpreted in the context of the nine Bradford-Hill criteria for causality, that is: (1) Strength of Association, (2) Consistency, (3) Specificity, (4) Temporality, (5) Biological Gradient, (6) Plausibility, (7) Experimental evidence, (8) Analogy, and (9) Coherence. These criteria necessitated referral to a body of literature wider than prospective cohort studies alone, especially in criteria 6 to 9. In this analysis, all nine of the Hill’s criteria were met for GI and GL indicating that we can be confident of a role for GI and GL as causal factors contributing to incident T2D. In addition, neither dietary fiber nor cereal fiber nor wholegrain were found to be reliable or effective surrogate measures of GI or GL. Finally, our cost–benefit analysis suggests food and nutrition advice favors lower GI or GL and would produce significant potential cost savings in national healthcare budgets. The high confidence in causal associations for incident T2D is sufficient to consider inclusion of GI and GL in food and nutrient-based recommendations. Keywords: causation; diabetes; glycemic index; glycemic load; dietary fiber; alcohol; cohort studies; epidemiology; meta-analysis; public health 1. Introduction Preventing type 2 diabetes (T2D) is a major goal of the World Health Organization, the International Diabetes Federation and many national organizations around the globe. There is scientific consensus that modifiable lifestyle choices influence a person’s risk of developing T2D. Known risk factors include energy intake, dietary fat, refined carbohydrate, refined grains, alcohol, dietary fiber, whole grains, overweight and obesity, physical activity and smoking [1]. It is possible that consuming diets in which the carbohydrate is too high in glycemic index (GI) or load (GL) may also constitute an increased risk, but the case for causality and the appropriate recommendations to include in public health guidelines are less certain. In the present context, GI reflects the glycemic impact of the carbohydrates in a diet (irrespective of quantity) while GL represents the overall glycemic impact taking into account both quantity and quality of carbohydrate. While it is established that lowering of fasting hyperglycemia can reduce both the risk of insulin resistance and β-cell dysfunction [2–4], it is questioned whether decreasing postprandial glycemia has similar effects. Moreover, it is controversial whether choosing lower GI or GL foods can reduce day-long postprandial glycemia. Thus, the question of cause-and-effect relationships between T2D and GI and GL remains. Here, we examine causality according to Bradford-Hill criteria [5], which is a recognized tool for interpreting both observational and interventional evidence, and the case for making dietary attributes (e.g., GI and GL) part of dietary recommendations [6–9]. We briefly include a cost–benefit consideration because this is also relevant to the justification for advising on a change of dietary habits within a population [10]. Critical to this inquiry is our updated systematic review with meta-analyses [11]. Nutrients 2019, 11, 1436 3 of 34 2. Methods 2.1. Bradford Hill-Criteria The Bradford-Hill criteria, otherwise known as Hill’s criteria for causation, are a group of guidelines that can be useful for providing evidence of a causal relationship between a putative cause and an effect, and were established by the English epidemiologist Sir Austin Bradford Hill in 1965 [5]. The criteria fall under headings or viewpoints: strength of association (from meta-analyses where possible), consistency of association, specificity, temporality, biological gradient (dose-dependency), plausibility, experimental evidence, analogy, and coherence. These criteria with original definitions are shown in Table1 alongside the definitions as used in the assessment of causation in the present analysis. For simplicity, a score of 1 was given to each criterion fulfilled. Confidence in causation “the most likely [probable] interpretation“ as stated by Hill [6] was considered to increase as the sum of criteria fulfilled increased from 1 to 9; scores of 1 to 3 are considered low, 4 to 6 moderate, and 7 to 9 high. 2.2. Biological Gradient (Dose-Response) Analysis Biological gradient (dose-response) meta-analyses for both GI and GL were undertaken using the generalized least-squares method for trend estimation of the dose-response data [12] (Stata software version 11.2 SE, 2009; StataCorp LP) using the pool first option [13], which is a one-step procedure. These were termed local dose-response analyses rather than the global dose-response analyses in [11] and were additional analyses to those undertaken in [11]. They differ in that local dose response meta-analyses have maximally overlapping exposures such that the combined study result gives the weighted mean RR within jurisdictions rather than across jurisdictions (areas of land having particular forms of law). Eligible prospective cohort
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